Title :
Quantitative structure-activity relationship (QSAR) modelling of N-aryl derivatives as cholinesterase inhibitors
Author :
Ridzuan, Mohd Syafiq Mohammad ; Jaafar, Mohd Zuli ; Zain, Mazatulikhma Mat
Author_Institution :
Fac. of Appl. Sci., Univ. Teknol. MARA, Shah Alam, Malaysia
Abstract :
A QSAR study on a series of N-aryl derivatives was performed to explore the important molecular descriptor which is responsible for their inhibitory activity towards choli nest erase enzyme, the common target for the treatment of Alzheimer´s disease. Molecular descriptors were calculated using DRAGON version 5.2 software Two methods of descriptor selection, stepwise regression and forward selection procedure, were performed and compared. Multiple Linear Regression (MLR) analysis was carried out to derive QSAR models, which were further evaluated for statistical significance and predictive power by leave-one-out (LOO) cross validation test. The best QSAR models against acetylcholinesterase and butylcholinesterase inhibitory activity were selected, having squared correlation coefficient R2=945% and 98.4%, and cross-validated squared correlation coefficient R2cv = 91.9% and 97.2%, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of more potent substituted N-aryl derivatives as cholinesterase inhibitor.
Keywords :
QSAR; correlation methods; diseases; enzymes; molecular biophysics; regression analysis; Alzheimer´s disease; DRAGON version 5.2 software; N-aryl derivatives; QSAR modelling; acetylcholinesterase inhibitory activity; butylcholinesterase inhibitory activity; cholinesterase inhibitors; enzyme; forward selection procedure; leave-one-out cross validation; molecular descriptor; multiple linear regression analysis; quantitative structure-activity relationship; squared correlation coefficient; stepwise regression; Biological system modeling; Compounds; Correlation; Drugs; Mathematical model; Predictive models; Software; Alzheimer´s disease; Multiple Linear Regression (MLR); N-aryl derivatives; QSAR;
Conference_Titel :
Humanities, Science and Engineering Research (SHUSER), 2012 IEEE Symposium on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4673-1311-7
DOI :
10.1109/SHUSER.2012.6269006